class_docstr: LightningModule wrapper which converts incoming floating point data in ``*_step`` and ``forward`` to double
  (``torch.float64``) precision.
  
  .. deprecated:: Use :meth:`~lightning.pytorch.core.hooks.ModelHooks.configure_model` instead.
  
  Args:
      pl_module: the model to wrap
code: def __init__(self, pl_module: "pl.LightningModule") -> None:
      super().__init__()
      rank_zero_deprecation(
          f"The `{type(self).__name__}` is deprecated and no longer needed. Convert the inputs to the `*_step`"
          f" methods directly using `trainer.precision_plugin.convert_input(...)`."
      )
      self.module = pl_module
  
      # set the parameters_to_ignore from LightningModule.
      _ddp_params_and_buffers_to_ignore = getattr(
          pl_module, "_ddp_params_and_buffers_to_ignore", []
      )
      self._ddp_params_and_buffers_to_ignore = [
          f"module.{p}" for p in _ddp_params_and_buffers_to_ignore
      ]
  
imports: from contextlib import contextmanager
  from typing import Any, ContextManager, Generator, Literal
  import torch
  import torch.nn
  from lightning_utilities.core.apply_func import apply_to_collection
  from torch import Tensor
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.plugins.precision.utils import _convert_fp_tensor, _DtypeContextManager
  from lightning.fabric.utilities.device_dtype_mixin import _DeviceDtypeModuleMixin
  from lightning.pytorch.plugins.precision.precision import Precision
  from lightning.pytorch.utilities.rank_zero import rank_zero_deprecation
label_desc: Use image node_id 1 to create a new LightningDoublePrecisionModule object from inherited base classes: _DeviceDtypeModuleMixin, nn with example: obj = LightningDoublePrecisionModule(pl_module)